Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Downloading mnist: 9.92MB [00:00, 22.4MB/s]                            
Extracting mnist: 100%|██████████| 60.0k/60.0k [00:08<00:00, 7.14kFile/s]
Downloading celeba: 1.44GB [01:06, 21.7MB/s]                               
Extracting celeba...

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f73ee453da0>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f73c0ae7208>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
/home/ec2-user/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
TensorFlow Version: 1.8.0
Default GPU Device: /device:GPU:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    inputs_real = tf.placeholder(tf.float32, shape=(None, image_width, image_height, image_channels))
    inputs_z = tf.placeholder(tf.float32, shape=(None, z_dim))
    learning_rate = tf.placeholder(tf.float32, shape=())
    
    return inputs_real, inputs_z, learning_rate

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [37]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    alpha = 0.2
    
    with tf.variable_scope('discriminator', reuse=reuse):
        x = tf.layers.dropout(images, rate=0.5)

        # Convolution Layer 1
        x1 = tf.layers.conv2d(x, 56, 5, strides=2, padding='same')
        relu1 = tf.maximum(alpha * x1, x1)
        
        # Convolution Layer 2
        x2 = tf.layers.conv2d(relu1, 112, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        
        # Convolution Layer 3
        x3 = tf.layers.conv2d(relu2, 224, 5, strides=2, padding='same')
        # bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * x3, x3)
        relu3 = tf.layers.dropout(relu3, rate=0.5)
        
        # Flatten
        x4 = tf.reshape(relu3, (-1, 4 * 4 * 224))
        logits = tf.layers.dense(x4, 1)
        out = tf.sigmoid(logits)
        
    return out, logits

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [47]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    alpha = 0.2
    
    with tf.variable_scope('generator', reuse=not(is_train)):
        # Fully Connected Layer 1
        x1 = tf.layers.dense(z, 7 * 7 * 112)
        x1 = tf.reshape(x1, (-1, 7, 7, 112))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        
        # Convolution Layer 2
        x2 = tf.layers.conv2d_transpose(x1, 56, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=is_train)
        relu2 = tf.maximum(alpha * bn2, bn2)
        
        # Convolution Layer 
        logits = tf.layers.conv2d_transpose(relu2, out_channel_dim, 5, strides=2, padding='same')
        out = tf.tanh(logits)
        
    return out

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [39]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    g_model = generator(input_z, out_channel_dim, is_train=True)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
    
    return d_loss, g_loss

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [40]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    
    for t in t_vars:
        assert t in d_vars or t in g_vars
        
    d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
    g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
    
    return d_train_opt, g_train_opt

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [41]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [42]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    steps = 0

    input_real, input_z, learning_rate_ = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])
    d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                steps += 1
                # Normalize
                batch_images = batch_images * 2
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, learning_rate_: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z, learning_rate_: learning_rate})
                
                if steps % 100 == 0:
                    d_train_loss = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    g_train_loss = g_loss.eval({input_z: batch_z})

                    print("Epoch Number: {}/{},".format(epoch_i + 1, epochs),
                          "Discriminator Loss: {:.6f},".format(d_train_loss),
                          "Generator Loss: {:.6f}".format(g_train_loss))

                    _ = show_generator_output(sess, batch_size, input_z, data_shape[3], data_image_mode)

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [45]:
batch_size = 32
z_dim = 100
learning_rate = 0.0001
beta1 = 0.1

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch Number: 1/2, Discriminator Loss: 0.159939, Generator Loss: 2.393975
Epoch Number: 1/2, Discriminator Loss: 0.373637, Generator Loss: 1.407621
Epoch Number: 1/2, Discriminator Loss: 2.523331, Generator Loss: 0.102065
Epoch Number: 1/2, Discriminator Loss: 0.553764, Generator Loss: 2.643696
Epoch Number: 1/2, Discriminator Loss: 0.752173, Generator Loss: 2.903723
Epoch Number: 1/2, Discriminator Loss: 0.806566, Generator Loss: 2.470970
Epoch Number: 1/2, Discriminator Loss: 0.594614, Generator Loss: 1.724775
Epoch Number: 1/2, Discriminator Loss: 0.710980, Generator Loss: 1.993919
Epoch Number: 1/2, Discriminator Loss: 0.757158, Generator Loss: 2.915231
Epoch Number: 1/2, Discriminator Loss: 0.575448, Generator Loss: 1.614861
Epoch Number: 1/2, Discriminator Loss: 0.580970, Generator Loss: 1.067185
Epoch Number: 1/2, Discriminator Loss: 0.762412, Generator Loss: 3.333040
Epoch Number: 1/2, Discriminator Loss: 0.390475, Generator Loss: 2.443178
Epoch Number: 1/2, Discriminator Loss: 0.419780, Generator Loss: 1.761073
Epoch Number: 1/2, Discriminator Loss: 1.220762, Generator Loss: 5.256454
Epoch Number: 1/2, Discriminator Loss: 0.390803, Generator Loss: 1.725488
Epoch Number: 1/2, Discriminator Loss: 0.448578, Generator Loss: 2.539802
Epoch Number: 1/2, Discriminator Loss: 0.519967, Generator Loss: 1.657253
Epoch Number: 2/2, Discriminator Loss: 0.435622, Generator Loss: 1.591731
Epoch Number: 2/2, Discriminator Loss: 0.513339, Generator Loss: 1.372079
Epoch Number: 2/2, Discriminator Loss: 0.433684, Generator Loss: 1.457621
Epoch Number: 2/2, Discriminator Loss: 0.556121, Generator Loss: 1.265240
Epoch Number: 2/2, Discriminator Loss: 0.461431, Generator Loss: 2.571841
Epoch Number: 2/2, Discriminator Loss: 0.561877, Generator Loss: 2.205790
Epoch Number: 2/2, Discriminator Loss: 1.827376, Generator Loss: 0.318159
Epoch Number: 2/2, Discriminator Loss: 0.601608, Generator Loss: 1.435734
Epoch Number: 2/2, Discriminator Loss: 0.572066, Generator Loss: 2.746703
Epoch Number: 2/2, Discriminator Loss: 0.526900, Generator Loss: 1.453279
Epoch Number: 2/2, Discriminator Loss: 0.504645, Generator Loss: 1.770234
Epoch Number: 2/2, Discriminator Loss: 0.574479, Generator Loss: 1.819924
Epoch Number: 2/2, Discriminator Loss: 0.551119, Generator Loss: 1.800971
Epoch Number: 2/2, Discriminator Loss: 0.592843, Generator Loss: 1.257765
Epoch Number: 2/2, Discriminator Loss: 0.544502, Generator Loss: 2.328463
Epoch Number: 2/2, Discriminator Loss: 0.612542, Generator Loss: 1.131816
Epoch Number: 2/2, Discriminator Loss: 0.460899, Generator Loss: 1.511343
Epoch Number: 2/2, Discriminator Loss: 0.632425, Generator Loss: 1.058913
Epoch Number: 2/2, Discriminator Loss: 0.504847, Generator Loss: 1.529832

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [48]:
batch_size = 32
z_dim = 100
learning_rate = 0.0001
beta1 = 0.1

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch Number: 1/1, Discriminator Loss: 0.178175, Generator Loss: 2.411363
Epoch Number: 1/1, Discriminator Loss: 0.535423, Generator Loss: 1.064638
Epoch Number: 1/1, Discriminator Loss: 0.532723, Generator Loss: 2.674185
Epoch Number: 1/1, Discriminator Loss: 1.384074, Generator Loss: 2.912534
Epoch Number: 1/1, Discriminator Loss: 0.712297, Generator Loss: 1.177724
Epoch Number: 1/1, Discriminator Loss: 0.756009, Generator Loss: 1.399116
Epoch Number: 1/1, Discriminator Loss: 0.765448, Generator Loss: 1.217380
Epoch Number: 1/1, Discriminator Loss: 0.942904, Generator Loss: 2.470527
Epoch Number: 1/1, Discriminator Loss: 0.863512, Generator Loss: 1.065217
Epoch Number: 1/1, Discriminator Loss: 0.608588, Generator Loss: 1.177256
Epoch Number: 1/1, Discriminator Loss: 0.753065, Generator Loss: 1.448233
Epoch Number: 1/1, Discriminator Loss: 0.782900, Generator Loss: 1.535707
Epoch Number: 1/1, Discriminator Loss: 0.660170, Generator Loss: 1.603085
Epoch Number: 1/1, Discriminator Loss: 0.750925, Generator Loss: 1.282675
Epoch Number: 1/1, Discriminator Loss: 0.818181, Generator Loss: 1.561276
Epoch Number: 1/1, Discriminator Loss: 0.966609, Generator Loss: 0.798635
Epoch Number: 1/1, Discriminator Loss: 0.743566, Generator Loss: 1.686886
Epoch Number: 1/1, Discriminator Loss: 1.057295, Generator Loss: 1.134387
Epoch Number: 1/1, Discriminator Loss: 0.918936, Generator Loss: 0.863459
Epoch Number: 1/1, Discriminator Loss: 1.178712, Generator Loss: 0.554538
Epoch Number: 1/1, Discriminator Loss: 0.896255, Generator Loss: 1.302347
Epoch Number: 1/1, Discriminator Loss: 1.073230, Generator Loss: 1.198938
Epoch Number: 1/1, Discriminator Loss: 1.026818, Generator Loss: 0.730924
Epoch Number: 1/1, Discriminator Loss: 0.999450, Generator Loss: 0.722317
Epoch Number: 1/1, Discriminator Loss: 0.887800, Generator Loss: 0.819262
Epoch Number: 1/1, Discriminator Loss: 1.095901, Generator Loss: 1.443176
Epoch Number: 1/1, Discriminator Loss: 0.980131, Generator Loss: 1.700871
Epoch Number: 1/1, Discriminator Loss: 0.963038, Generator Loss: 1.115017
Epoch Number: 1/1, Discriminator Loss: 1.091148, Generator Loss: 0.597137
Epoch Number: 1/1, Discriminator Loss: 1.049010, Generator Loss: 1.603471
Epoch Number: 1/1, Discriminator Loss: 0.952991, Generator Loss: 0.913708
Epoch Number: 1/1, Discriminator Loss: 1.270141, Generator Loss: 1.851017
Epoch Number: 1/1, Discriminator Loss: 0.973213, Generator Loss: 0.966683
Epoch Number: 1/1, Discriminator Loss: 1.014048, Generator Loss: 0.775090
Epoch Number: 1/1, Discriminator Loss: 0.935242, Generator Loss: 1.107219
Epoch Number: 1/1, Discriminator Loss: 0.950919, Generator Loss: 1.007286
Epoch Number: 1/1, Discriminator Loss: 1.018238, Generator Loss: 1.068812
Epoch Number: 1/1, Discriminator Loss: 0.943941, Generator Loss: 0.901055
Epoch Number: 1/1, Discriminator Loss: 1.036993, Generator Loss: 1.076386
Epoch Number: 1/1, Discriminator Loss: 0.928621, Generator Loss: 1.266353
Epoch Number: 1/1, Discriminator Loss: 0.875578, Generator Loss: 1.054017
Epoch Number: 1/1, Discriminator Loss: 0.867534, Generator Loss: 1.929714
Epoch Number: 1/1, Discriminator Loss: 0.853230, Generator Loss: 0.973954
Epoch Number: 1/1, Discriminator Loss: 0.879156, Generator Loss: 0.748021
Epoch Number: 1/1, Discriminator Loss: 0.843290, Generator Loss: 1.061979
Epoch Number: 1/1, Discriminator Loss: 0.925286, Generator Loss: 0.964442
Epoch Number: 1/1, Discriminator Loss: 0.815899, Generator Loss: 1.457928
Epoch Number: 1/1, Discriminator Loss: 0.978196, Generator Loss: 0.714570
Epoch Number: 1/1, Discriminator Loss: 0.858063, Generator Loss: 1.295445
Epoch Number: 1/1, Discriminator Loss: 0.906404, Generator Loss: 0.952493
Epoch Number: 1/1, Discriminator Loss: 1.080959, Generator Loss: 1.259267
Epoch Number: 1/1, Discriminator Loss: 1.073576, Generator Loss: 0.611298
Epoch Number: 1/1, Discriminator Loss: 0.866358, Generator Loss: 1.297771
Epoch Number: 1/1, Discriminator Loss: 0.826262, Generator Loss: 0.869954
Epoch Number: 1/1, Discriminator Loss: 0.867301, Generator Loss: 0.872993
Epoch Number: 1/1, Discriminator Loss: 0.958649, Generator Loss: 0.701434
Epoch Number: 1/1, Discriminator Loss: 0.939109, Generator Loss: 0.956906
Epoch Number: 1/1, Discriminator Loss: 0.865152, Generator Loss: 1.228676
Epoch Number: 1/1, Discriminator Loss: 1.054810, Generator Loss: 0.721867
Epoch Number: 1/1, Discriminator Loss: 0.844806, Generator Loss: 1.274882
Epoch Number: 1/1, Discriminator Loss: 0.881465, Generator Loss: 1.073656
Epoch Number: 1/1, Discriminator Loss: 0.855773, Generator Loss: 0.992071
Epoch Number: 1/1, Discriminator Loss: 0.971088, Generator Loss: 0.770804

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.